Identification of Molecular PD Endophenotypes Royal College of Surgeons in Ireland

Parkinson's Disease (PD) is a heterogeneous neurological condition, and there is significant demand for peripheral biomarkers for patient stratification. The aim of this project is to apply data integration and network inference approaches to derive PD endophenotype signatures for blood-based stratification of PD patients. Specific objectives: 1) Identify multi-modal clusters of co-expressed genes/proteins and gene-regulatory networks associated with disease state; 2) Derive peripheral PD endophenotype signatures; 3) Refine gene signatures for future clinical application. The student will first apply correlation-based network inference approaches on independent multi-‘omic datasets (transcriptome, proteome) from LuxPark and PPMI to identify clusters of co-expressed genes/proteins using unsupervised learning approaches (e.g., MOFA+, clustering, correlation analyses). Data integration approaches incorporating specific genomic markers (e.g., GWAS hits, mutation rates) will then be used to construct multi-modal clusters and infer de novo regulatory networks. Clusters/networks will be correlated with clinical data (disease diagnosis, symptoms, stage) to identify functional clusters associated with particular disease states. Critical regulatory nodes/pathways will be identified by integrating differential expression and network analysis (e.g., centrality). At Petanux, the student will apply AI/ML and XAI techniques that can predict an individual’s disease molecular disease endotype.

Supervisors:

• Niamh Connolly ( https://www.rcsi.com/people/profile/niamhmconnolly )
• Prof. Mahdi Bohlouli, Petanux
• Prof Jochen Prehn ( https://www.rcsi.com/people/profile/JPrehn )

Mandatory Specifications:

We are seeking a highly skilled and motivated Research Scientist with a demonstrable background in Data Science (M.Sc. with excellent grades in Computer Science, Bioinformatics, Mathematics, or a similar subject). The successful candidate should have a passion for research on computational methods in medicine.

Desirable Specifications:

• Clear understanding of statistical and mathematical concepts underlying Data Science.
• Knowledge of Parkinson’s biology, biomarker research or patient stratification is a plus.
• Strong background in AI/ML.
• Experience with biomedical data is highly desirable.
• Familiarity with XAI techniques and causal inference methods is a plus.
• Demonstrable proficiency in R and Python programming and using libraries such as PyTorch.
• Ability to work independently.
• Very good English speaking and writing skills.
• Very good communication skills.

Applications:

To apply, please access the following link and complete the application form in full: https://euraxess.ec.europa.eu/jobs/290899

Unfortunately, we are unable to provide individual feedback to applicants.

Shortlisted candidates will be invited for interview.

At this stage only successful candidates will be contacted to submit CV, transcripts and other relevant documentation.

For successful candidates, referees will also be contacted at this stage for a reference.

Applications must include:

1. a completed application form
2. English language requirements – see https://www.rcsi.com/dublin/postgraduate/policies-and-guidelines/english-language-requirements .

All applications must be made online by 28th February 2025

It is the candidate’s responsibility to ensure the application form is completed in full and on time. Late and/or incomplete applications will not normally be assessed.

Duration: 36 months (3 years)

Application Deadline: 28th February 2025

Shortlisting: March 2025

Interviews: March-April 2025 (virtual interviews available)

Start Date: October 2025

Further information on the Royal College of Surgeons in Ireland can be found here: https://www.rcsi.com/